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The Fundamental Research Funding Program provides a framework for fostering multidisciplinary research in data science and by confirming and strengthening the IVADO community as a key player in the field. This program is also very focused on the future, encouraging the training of future data researchers and the creation of the scientific foundations of tomorrow’s fundamental and applied research.<\/p>\n
IVADO\u2019s commitment to equity, diversity and inclusion and note to applicants<\/strong> The goals of this program are to:<\/p>\n The fundamental research funding program supports research on the issues raised in the Canada First Excellence funding competition<\/a>: data science in a broad sense, including methodological research in data science (machine learning, operations research, statistics) and their applications in various fields.<\/p>\n The research supported by this program must be collaborative and multidisciplinary, whether through several methodological approaches (machine learning, operational research, decision sciences) or through combining a methodological approach with an application field. Whether or not a research project is applied has no impact on the eligibility of projects. But projects are expected to make a fundamental contribution in data science and applications must clearly emphasize and demonstrate this aspect.<\/p>\n <\/p>\n<\/div> The goals of this program are to:<\/p>\n The fundamental research funding program supports fundamental research concerning the issues described in the awarded Apog\u00e9e\/CFREF grant<\/a>: data science in a broad sense, including methodological research in data science (machine learning, operations research, statistics) and their applications in various fields.<\/p>\n To achieve the objectives described in this funding request, the scientific strategy focuses on two fundamental aspects of intelligent behaviour: knowledge and the ability to use it, or, in other words, on understanding, forecasting and decision-making processes. The emphasis is therefore on the development of tools, methods, algorithms and models, ideally suited for use in a wide variety of application fields.<\/strong> Applied research projects are encouraged, particularly in priority areas (health, transport, logistics, resources and energy, information services and trade) but not limited to these priority areas.<\/p>\n The research supported by this program must be collaborative and multidisciplinary, whether through several methodological approaches (machine learning, operational research, decision sciences) or through combining a methodological approach with an application field. Whether or not a research project is applied has no impact on the eligibility of projects. But projects are expected to make a fundamental contribution in data science and applications must clearly emphasize and demonstrate this aspect.<\/p>\n For this second competition, a funding between $100k and $150k per year for two years will be awarded to the selected projects. So, the maximum allowable is $300k spread over two years. A portion of the total available budget has to be allocated to student scholarships (graduate and undergraduate students) and postdoctoral fellows; applicants must submit a budget in which at least two-thirds (66%) falls into this category.<\/p>\n Generally, the Tri-Agency Financial Administration Guide <\/i>(http:\/\/www.nserc-crsng.gc.ca\/professors-professeurs\/financialadminguide-guideadminfinancier\/index_eng.asp<\/a> ) and the rules of the Apog\u00e9e\/CFREF program <\/i>(http:\/\/www.cfref-apogee.gc.ca\/program-programme\/admin_guide-guide_administration-eng.aspx<\/a> ) will serve as guides for the program.<\/p>\n This program allows for the funding of:<\/p>\n <\/p>\n Proposals will go through an administrative screening. The review committee will not receive applications:<\/p>\n The evaluation of projects will be based in equal parts on the one hand:<\/p>\n and on the other:<\/p>\n Each application will be assigned to four reviewers, chosen from among professors familiar with the field of the application. Direct collaborators (co-supervisors or recent co-publications) of applicant team members will not participate in the evaluations of these proposals.<\/p>\n Applications will then be divided into review sub-committees by methodological area, in order to establish a ranking based on excellence. Each sub-committee will be composed of three professors from three different universities, including a chair.<\/p>\n The rankings of the sub-committees will be pooled at a meeting of the Chairs to determine a ranking of the applications. The chairs will propose a list of projects to be funded according to the available budget. The results will be discussed and approved by the Scientific Committee at a special meeting of the Scientific Committee.<\/p>\n Instruction for submitting an application are available under the “Submission” tab.<\/p>\n Elements of the application:<\/strong><\/p>\n At the end of the two-year funding, the principal investigator must submit a final report including:<\/p>\n Q:<\/strong> What happens if my application file does not meet the requested length?<\/p>\n R:<\/strong> Your application will automatically be disqualified.<\/p>\n <\/p>\n Q:<\/strong> When will I get a reply on my application?<\/p>\n R:<\/strong> By April 2020<\/span> (see section “Description” for more details)<\/p>\n <\/p>\n More questions? Please send them to: programmes-excellence@ivado.ca<\/a><\/p>\n<\/div> Please apply through: https:\/\/ivado.smapply.io\/<\/a><\/p>\n The elements of the application are:<\/p>\n All documents must be uploaded to our dedicated submission platform.<\/p>\n<\/div>
\nTo ensure all members of society draw equal benefit from the advancement of knowledge and opportunities in data science, IVADO promotes equity, diversity and inclusion through each of its programs. IVADO aims to provide a recruitment process and research setting that are inclusive, non-discriminatory, open and transparent.<\/p>\nOverview<\/span><\/h4>
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\nThe decision to accept or refuse an organ for transplantation is an important one, as the donor’s characteristics are strongly associated with the long-term survival of the transplanted kidney. In partnership with their health care provider, the transplant candidates need to answer two questions: (1) How long is the kidney from this specific donor expected to last for me? (2) If I refuse this specific donor, how much longer am I expected to wait before getting a better kidney?
\nWe propose to use deep learning to predict the success of a possible matching. The results will contribute to build a clinical decision support tool answering the two questions above and helping transplant physicians and candidates to make the best decision. In addition, the quality of the matching can be the input of optimization algorithms designed to improve social welfare of organ allocations.<\/li>\n<\/ul>\n<\/li>\n\n
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\nOur goal: Develop models to understand the effects of mutations, genome-wide, on cognition, brain structure and connectivity.
\nModels will be developed using large cohorts of individuals for whom, genetic, cognitive and neuroimaging data was collected.
\nDeliverable: Algorithms allowing clinicians to understand the contribution of mutations to the neurodevelopmental symptoms observed in their patients.<\/li>\n<\/ul>\n<\/li>\n\n
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\nof service or as a metric influencing travel decisions. This proposal tackles the issue of travel time prediction from several angles: i) data pre-processing (map-matching), ii) short-term travel time prediction and iii) long-term travel time prediction. These tasks will require the development of new approaches in statistical and machine learning to adequately model GPS trajectory data and to quantify the prediction error.<\/li>\n<\/ul>\n<\/li>\n\n
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